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SUMMARY:Inference from Evolving Populations: Agriculture - Maud Lemercier 
  (University of Warwick\; The Alan Turing Institute)
DTSTART:20210316T110000Z
DTEND:20210316T112500Z
UID:TALK157912@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:Inferring properties about time-evolving populations is a wide
 spread problem\, yet a non-standard machine learning task. Most existing m
 achine learning models can either handle a static snapshot of a population
  or a single trajectory. In this talk I will present a generic framework\,
  based on the expected signature which enables to compactly summarize a cl
 oud of time series and make decisions on it. I will discuss an application
  in agricultural monitoring\, where a key challenge is to predict the yiel
 d before harvest using a collection of time series acquired by satellite-s
 ensors.
LOCATION:Seminar Room 1\, Newton Institute
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